1. Field of the Invention
The present invention relates generally to facilitating selection of a value of a data element and more particularly to methods, systems and machine-readable mediums for arbitrating among values of a data element from regulated and non-regulated data sources.
2. Description of the Related Art
In information sharing and processing environments, many applications have been developed to process information for purposes such as making a decision or evaluating a set of information based on one or more criteria. In many cases, the reliability of these applications is often limited by the ability to reliably acquire accurate information. For example, acquiring accurate customer information is essential for businesses to serve their customers efficiently. The customer information includes, for example, various data elements like demographic information such as postal address, age, year of birth, and customer history such as credit history or purchase history. The evolution of distributed network environments such as the Internet has resulted in an explosion of both the quantity and availability of the customer information from various sources. These data sources can be regulated data sources such as credit bureaus, consumer reporting agencies or non-regulated data sources such as banks, mortgage issuers, credit union lenders, property lease information repositories and customer surveys conducted by various business units. Typically, the regulated data sources are considered more accurate and reliable compared to the non-regulated data sources. However, there is no assurance that the customer information derived from any data source is accurate or reliable. Furthermore, given the large volume of data, it is typically very difficult, time consuming and expensive to verify the accuracy of the data with the customers themselves.
Generally, organizations rely on cumbersome manual assessment of data by their employees to select a value of a data element among those obtained from various data sources. For example, the employees manually select a first value of birth year data of a customer from a first data source, and discard a second value from a second data source if the two data sources contain non-identical values. Some organizations use automated tools to select a value from more than one data source. However, such techniques often fail to consider the reliability of a data source in order to select the most accurate customer information. Similarly, such techniques seldom take into consideration that, while a given data source may generally be deemed to be reliable over a second data source, the second data source may be more reliable for information for certain data elements. For example, a first data source may contain the more accurate customer address data, while a second data source may contain more accurate date of birth data. Thus, when reliability of data is considered at the data source level and not by a data element by data element basis, a less reliable value may be used instead of more reliable data.
Further, other systems usually do not take into account the known reliance on the regulated data sources by many financial institutions. Financial institutions may prefer a value from the regulated data sources such as credit bureaus and consumer rating agencies over the non-regulated data sources such as lenders, creditors and utilities. Additionally, some modern organizations not only require the most reliable data available, but also a qualitative indicator of reliability of the data. For example, a qualitative indicator may be used by a human analyst in risk assessment divisions of an organization to make more informed decisions.
Therefore, a long felt need exists for a method and system that overcomes these and other problems associated with current data arbitration and related data analysis methods. A need exists to select the most reliable value of a data element from multiple values of the data element obtained from multiple data sources.